Add paddle model and use InfiniTensor to infer

examples/python/paddle_densenet.py

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+
+import paddle
+import numpy as np
+import paddle.vision.transforms as T
+from paddle.vision.datasets import Cifar10
+from pyinfinitensor.onnx import OnnxStub, backend
+import onnx
+import itertools
+
+def run_mnist_train_and_infer():
+    
+    paddle.device.set_device("gpu")
+
+    transform = T.Compose(
+        [
+            T.Resize(224),
+            T.ToTensor(),
+            T.Normalize(
+                mean=[0.5, 0.5, 0.5],
+                std=[0.5, 0.5, 0.5],
+                to_rgb=True,
+            ),
+        ]
+    )
+    
+    # 下载数据集并初始化 DataSet
+    train_dataset = paddle.vision.datasets.Cifar10(mode='train', transform=transform)
+    test_dataset = paddle.vision.datasets.Cifar10(mode='test', transform=transform)
+
+    # 模型组网并初始化网络
+    densenet = paddle.vision.models.DenseNet(num_classes=10)
+    model = paddle.Model(densenet)
+
+    # 模型训练的配置准备，准备损失函数，优化器和评价指标
+    model.prepare(paddle.optimizer.Adam(parameters=model.parameters()), 
+                paddle.nn.CrossEntropyLoss(),
+                paddle.metric.Accuracy())
+
+    # 模型训练
+    model.fit(train_dataset, epochs=5, batch_size=64, verbose=1)
+    # 模型评估
+    model.evaluate(test_dataset, batch_size=64, verbose=1)
+
+    # export to ONNX
+    save_path = 'onnx.save/densenet' # 需要保存的路径
+    x_spec = paddle.static.InputSpec([1, 3, 224, 224], 'float32', 'x') # 为模型指定输入的形状和数据类型，支持持 Tensor 或 InputSpec ，InputSpec 支持动态的 shape。
+    paddle.onnx.export(densenet, save_path, input_spec=[x_spec], opset_version=11)
+
+    # 加载onnx模型并放到Infinitensor中
+    model_path = save_path + ".onnx"
+    onnx_model = onnx.load(model_path)
+    gofusion_model = OnnxStub(onnx_model, backend.cuda_runtime())
+    model = gofusion_model
+    model.init()
+
+    # 启动推理
+    cifar10_test = Cifar10(
+        mode="test",
+        transform=transform,  # apply transform to every image
+        backend="cv2",  # use OpenCV as image transform backend
+    )
+    batch_size = 1
+    total_size = 0
+    total_acc = 0.0
+    for data in itertools.islice(iter(cifar10_test), 10000):
+        images, labels = data
+        next(model.inputs.items().__iter__())[1].copyin_float(images.reshape([3*224*224]).tolist())
+        model.run()
+        outputs = next(model.outputs.items().__iter__())[1].copyout_float()
+        outputs = paddle.to_tensor(outputs)
+        outputs = paddle.reshape(outputs, (1, 10))
+        labels = paddle.to_tensor(labels)
+        labels = paddle.reshape(labels, (1,1))
+        acc = paddle.metric.accuracy(outputs, labels)
+        total_acc += acc
+        total_size += batch_size
+    print("test acc: {}".format(total_acc.numpy() / total_size))
+
+
+if __name__ == "__main__":
+    run_mnist_train_and_infer()

examples/python/paddle_inception.py

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+import paddle
+import numpy as np
+import paddle.vision.transforms as T
+from paddle.vision.datasets import Cifar10
+from pyinfinitensor.onnx import OnnxStub, backend
+import onnx
+import itertools
+
+def run_mnist_train_and_infer():
+    
+    paddle.device.set_device("gpu")
+
+    transform = T.Compose(
+        [
+            T.Resize(224),
+            T.ToTensor(),
+            T.Normalize(
+                mean=[0.5, 0.5, 0.5],
+                std=[0.5, 0.5, 0.5],
+                to_rgb=True,
+            ),
+        ]
+    )
+    
+    # 下载数据集并初始化 DataSet
+    train_dataset = paddle.vision.datasets.Cifar10(mode='train', transform=transform)
+    test_dataset = paddle.vision.datasets.Cifar10(mode='test', transform=transform)
+
+    # 模型组网并初始化网络
+    inception = paddle.vision.models.InceptionV3(num_classes=10)
+    model = paddle.Model(inception)
+
+    # 模型训练的配置准备，准备损失函数，优化器和评价指标
+    model.prepare(paddle.optimizer.Adam(parameters=model.parameters()), 
+                paddle.nn.CrossEntropyLoss(),
+                paddle.metric.Accuracy())
+
+    # 模型训练
+    model.fit(train_dataset, epochs=5, batch_size=64, verbose=1)
+    # 模型评估
+    model.evaluate(test_dataset, batch_size=64, verbose=1)
+
+    # export to ONNX
+    save_path = 'onnx.save/inception' # 需要保存的路径
+    x_spec = paddle.static.InputSpec([1, 3, 224, 224], 'float32', 'x') # 为模型指定输入的形状和数据类型，支持持 Tensor 或 InputSpec ，InputSpec 支持动态的 shape。
+    paddle.onnx.export(inception, save_path, input_spec=[x_spec], opset_version=11)
+
+    # 加载onnx模型并放到Infinitensor中
+    model_path = save_path + ".onnx"
+    onnx_model = onnx.load(model_path)
+    gofusion_model = OnnxStub(onnx_model, backend.cuda_runtime())
+    model = gofusion_model
+    model.init()
+
+    # 启动推理
+    cifar10_test = Cifar10(
+        mode="test",
+        transform=transform,  # apply transform to every image
+        backend="cv2",  # use OpenCV as image transform backend
+    )
+    batch_size = 1
+    total_size = 0
+    total_acc = 0.0
+    for data in itertools.islice(iter(cifar10_test), 10000):
+        images, labels = data
+        next(model.inputs.items().__iter__())[1].copyin_float(images.reshape([3*224*224]).tolist())
+        model.run()
+        outputs = next(model.outputs.items().__iter__())[1].copyout_float()
+        outputs = paddle.to_tensor(outputs)
+        outputs = paddle.reshape(outputs, (1, 10))
+        labels = paddle.to_tensor(labels)
+        labels = paddle.reshape(labels, (1,1))
+        acc = paddle.metric.accuracy(outputs, labels)
+        total_acc += acc
+        total_size += batch_size
+    print("test acc: {}".format(total_acc.numpy() / total_size))
+
+
+
+if __name__ == "__main__":
+    run_mnist_train_and_infer() 

examples/python/paddle_resnet.py

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+
+import paddle
+import numpy as np
+import paddle.vision.transforms as T
+from paddle.vision.datasets import Cifar10
+from pyinfinitensor.onnx import OnnxStub, backend
+import onnx
+import itertools
+from paddle.vision.models.resnet import BasicBlock
+
+def run_mnist_train_and_infer():
+    
+    paddle.device.set_device("gpu")
+
+    transform = T.Compose(
+        [
+            T.Resize(224),
+            T.ToTensor(),
+            T.Normalize(
+                mean=[0.5, 0.5, 0.5],
+                std=[0.5, 0.5, 0.5],
+                to_rgb=True,
+            ),
+        ]
+    )
+    
+    # 下载数据集并初始化 DataSet
+    train_dataset = paddle.vision.datasets.Cifar10(mode='train', transform=transform)
+    test_dataset = paddle.vision.datasets.Cifar10(mode='test', transform=transform)
+
+    # 模型组网并初始化网络
+    resnet = paddle.vision.models.ResNet(BasicBlock, depth=18, num_classes=10)
+    model = paddle.Model(resnet)
+
+    # 模型训练的配置准备，准备损失函数，优化器和评价指标
+    model.prepare(paddle.optimizer.Adam(parameters=model.parameters()), 
+                paddle.nn.CrossEntropyLoss(),
+                paddle.metric.Accuracy())
+
+    # 模型训练
+    model.fit(train_dataset, epochs=5, batch_size=64, verbose=1)
+    # 模型评估
+    model.evaluate(test_dataset, batch_size=64, verbose=1)
+
+    # export to ONNX
+    save_path = 'onnx.save/resnet' # 需要保存的路径
+    x_spec = paddle.static.InputSpec([1, 3, 224, 224], 'float32', 'x') # 为模型指定输入的形状和数据类型，支持持 Tensor 或 InputSpec ，InputSpec 支持动态的 shape。
+    paddle.onnx.export(resnet, save_path, input_spec=[x_spec], opset_version=11)
+
+    # 加载onnx模型并放到Infinitensor中
+    model_path = save_path + ".onnx"
+    onnx_model = onnx.load(model_path)
+    gofusion_model = OnnxStub(onnx_model, backend.cuda_runtime())
+    model = gofusion_model
+    model.init()
+
+    # 启动推理
+    cifar10_test = Cifar10(
+        mode="test",
+        transform=transform,  # apply transform to every image
+        backend="cv2",  # use OpenCV as image transform backend
+    )
+    batch_size = 1
+    total_size = 0
+    total_acc = 0.0
+    for data in itertools.islice(iter(cifar10_test), 10000):
+        images, labels = data
+        next(model.inputs.items().__iter__())[1].copyin_float(images.reshape([3*224*224]).tolist())
+        model.run()
+        outputs = next(model.outputs.items().__iter__())[1].copyout_float()
+        outputs = paddle.to_tensor(outputs)
+        outputs = paddle.reshape(outputs, (1, 10))
+        labels = paddle.to_tensor(labels)
+        labels = paddle.reshape(labels, (1,1))
+        acc = paddle.metric.accuracy(outputs, labels)
+        total_acc += acc
+        total_size += batch_size
+    print("test acc: {}".format(total_acc.numpy() / total_size))
+
+
+if __name__ == "__main__":
+    run_mnist_train_and_infer()